SearcharxivSearch

arXiv subjects

Omer T. Inan

Publications and source records attributed to Omer T. Inan.

3 recordsLinked to original sources

Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study

Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a cohort that no longer reflects contemporary critical care. No alternative learned directly from patient trajectories is in routine use. We conducted a retrospective two-cohort study on a total of 29,116 and 7,691 adult patients meeting Sepsis-3 criteria from two hospital systems in Massachusetts and Georgie, respectively. We developed a sepsis index using 43 routinely charted variables over a 72-hour treatment window. Unlike previous studies, we use mortality as a treatment-level ranking signal rather than a per-state target, allowing credit to be redistributed non-uniformly across timesteps. Evaluation was done on a permanent 20% test holdout, using clinical vignettes and Spearman correlation. Uncertainty intervals were obtained by bootstrap resampling of whole patients. Under this ranking scheme, non-survivors scored 1.19-1.64 points higher than survivors on a 0-10 scale within all strata of baseline SOFA-2, with similar results stratifying within lactate, mean arterial pressure (MAP), and creatinine. Within-patient change in the index correlated with change in lactate (Spearman rho = 0.39; n = 1,854). Similar, weaker correlations were found for MAP and creatinine. On a cohort level, cross-institutional agreement measured by Spearman correlation between models trained on different sites, were 70-77% of same-site correlation. External within-patient correlations were 0.54 and 0.59 against ceilings of 0.92 and 0.90. Our index also correlated with established indices, while null controls stayed near zero. Our index demonstrated hourly prognostic information that meaningfully separates patient outcomes and is consistent with clinical expectation, indicating potential as a decision support tool complementing clinical judgement.

cs.AI

Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure

Objective: Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization. Early detection of HF symptoms and pro-active management may reduce adverse events. Approach: Twenty-eight participants were monitored using a smartphone app after discharge from hospitals, and each clinical event during the enrollment (N=110 clinical events) was recorded. Motion, social, location, and clinical survey data collected via the smartphone-based monitoring system were used to develop and validate an algorithm for predicting or classifying HF decompensation events (hospitalizations or clinic visit) versus clinic monitoring visits in which they were determined to be compensated or stable. Models based on single modality as well as early and late fusion approaches combining patient-reported outcomes and passive smartphone data were evaluated. Results: The highest AUCPr for classifying decompensation with a late fusion approach was 0.80 using leave one subject out cross-validation. Significance: Passively collected data from smartphones, especially when combined with weekly patient-reported outcomes, may reflect behavioral and physiological changes due to HF and thus could enable prediction of HF decompensation.

eess.SP

Detecting Aortic Valve Opening and Closing from Distal Body Vibrations

Objective: Proximal and whole-body vibrations are well studied in seismocardiography and ballistocardiography, yet distal vibrations are still poorly understood. In this paper we develop two methods to measure aortic valve opening (AVO) and closing (AVC) from distal vibrations. Methods: AVO and AVC were detected for each heartbeat with accelerometers on the upper arm (A), wrist (W), and knee (K) of 22 consenting adults following isometric exercise. Exercise-induced changes were recorded with impedance cardiography, and nine-beat ensemble averaging was applied. Our first method, FilterBCG, detects peaks in distal vibrations after filtering with individually-tuned bandpass filters while RidgeBCG uses ridge regression to estimate AVO and AVC without peaks. Pseudocode is provided. Results: In agreement with recent studies, we did not find peaks at AVO and AVC in distal vibrations, and the conventional R-J interval method from the literature also correlated poorly with AVO (r2 = 0.22 A, 0.14 W, 0.12 K). Interestingly, distal vibrations filtered with FilterBCG resembled seismocardiogram signals and yielded reliable peaks at AVO (r2 = 0.95 A, 0.94 W, 0.77 K) and AVC (r2 = 0.92 A, 0.89 W, 0.68 K). Conclusion: FilterBCG measures AVO and AVC accurately from arm, wrist, and knee vibrations, and it outperforms R-J intervals and RidgeBCG. Significance: To our knowledge, this study is the first to measure AVC accurately from distal vibrations. Finally, AVO timing is needed to assess cardiovascular disease risk with pulse wave velocity (PWV) and for cuff-less diastolic blood pressure measurement via aortic pulse-transit time (PTT).

physics.med-ph